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Effective communication is an important skill for enabling informationexchange in multi-agent settings and emergent communication is now a vibrantfield of research, with common settings involving discrete cheap-talk channels.Since, by definition, these settings involve arbitrary encoding of information,typically they do not allow for the learned protocols to generalize beyondtraining partners. In contrast, in this work, we present a novel problemsetting and the Quasi-Equivalence Discovery (QED) algorithm that allows forzero-shot coordination (ZSC), i.e., discovering protocols that can generalizeto independently trained agents. Real world problem settings often containcostly communication channels, e.g., robots have to physically move theirlimbs, and a non-uniform distribution over intents. We show that these twofactors lead to unique optimal ZSC policies in referential games, where agentsuse the energy cost of the messages to communicate intent. Other-Play wasrecently introduced for learning optimal ZSC policies, but requires prioraccess to the symmetries of the problem. Instead, QED can iteratively discoversthe symmetries in this setting and converges to the optimal ZSC policy.

Quasi-Equivalence Discovery for Zero-Shot Emergent Communication